CN116564526A - Bone health assessment method, device and storage medium - Google Patents
Bone health assessment method, device and storage medium Download PDFInfo
- Publication number
- CN116564526A CN116564526A CN202310841298.8A CN202310841298A CN116564526A CN 116564526 A CN116564526 A CN 116564526A CN 202310841298 A CN202310841298 A CN 202310841298A CN 116564526 A CN116564526 A CN 116564526A
- Authority
- CN
- China
- Prior art keywords
- patient
- motion
- fracture
- motion state
- vital sign
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000037180 bone health Effects 0.000 title claims abstract description 46
- 238000000034 method Methods 0.000 title claims abstract description 25
- 230000008859 change Effects 0.000 claims description 38
- 230000010365 information processing Effects 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 3
- 210000000988 bone and bone Anatomy 0.000 abstract description 6
- 238000012549 training Methods 0.000 abstract description 3
- 230000036541 health Effects 0.000 abstract description 2
- 208000001132 Osteoporosis Diseases 0.000 description 6
- 201000010099 disease Diseases 0.000 description 4
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 4
- 238000005728 strengthening Methods 0.000 description 4
- 238000011156 evaluation Methods 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 102000009027 Albumins Human genes 0.000 description 1
- 108010088751 Albumins Proteins 0.000 description 1
- 102000002260 Alkaline Phosphatase Human genes 0.000 description 1
- 108020004774 Alkaline Phosphatase Proteins 0.000 description 1
- 206010065687 Bone loss Diseases 0.000 description 1
- 206010006187 Breast cancer Diseases 0.000 description 1
- 208000026310 Breast neoplasm Diseases 0.000 description 1
- OYPRJOBELJOOCE-UHFFFAOYSA-N Calcium Chemical compound [Ca] OYPRJOBELJOOCE-UHFFFAOYSA-N 0.000 description 1
- 102000001554 Hemoglobins Human genes 0.000 description 1
- 108010054147 Hemoglobins Proteins 0.000 description 1
- 206010033128 Ovarian cancer Diseases 0.000 description 1
- 206010061535 Ovarian neoplasm Diseases 0.000 description 1
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000032683 aging Effects 0.000 description 1
- 239000003098 androgen Substances 0.000 description 1
- 230000037396 body weight Effects 0.000 description 1
- 230000037182 bone density Effects 0.000 description 1
- 239000011575 calcium Substances 0.000 description 1
- 229910052791 calcium Inorganic materials 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 239000000262 estrogen Substances 0.000 description 1
- 229940011871 estrogen Drugs 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 230000008338 local blood flow Effects 0.000 description 1
- 210000004698 lymphocyte Anatomy 0.000 description 1
- 230000009245 menopause Effects 0.000 description 1
- 210000003205 muscle Anatomy 0.000 description 1
- 201000004228 ovarian endometrial cancer Diseases 0.000 description 1
- 239000011574 phosphorus Substances 0.000 description 1
- 229910052698 phosphorus Inorganic materials 0.000 description 1
- 230000035935 pregnancy Effects 0.000 description 1
- 210000000689 upper leg Anatomy 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/30—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
Landscapes
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Epidemiology (AREA)
- Data Mining & Analysis (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Biomedical Technology (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biophysics (AREA)
- Physical Education & Sports Medicine (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
Abstract
The invention discloses a bone health assessment method, a bone health assessment device and a storage medium, which belong to the technical field of health state assessment and comprise the steps of acquiring vital sign data of a patient in a first motion state, a second motion state and a third motion state; predicting rough probability intervals of fracture of the patient in different motion states according to a first prediction model based on vital sign data of the patient in different motion states; acquiring gender information and characteristic information about the gender of the patient, and determining a precise probability interval based on the predicted rough probability interval according to a second prediction model; based on the predicted accurate probability interval of the fracture of the patient, the accurate probability interval of the fracture of the patient in different motion states is determined, and the motion guidance opinion is provided. In order to enable the bone to be healthier for training, exercise guidance comments are provided, and exercise time is controlled, so that a patient can be trained safely, and physique is enhanced.
Description
Technical Field
The invention relates to the technical field of health state evaluation, in particular to a bone health evaluation method, a bone health evaluation device and a storage medium.
Background
With the acceleration of aging of the population, osteoporosis has become a major disease afflicting the elderly population. About 2 hundred million people worldwide suffer from osteoporosis, and the incidence rate of the osteoporosis is the seventh most common disease and frequently occurring disease. Investigation showed that in the population over 50 years old, women had 4 times the chance of osteoporosis and 2 times the chance of bone loss. In addition, the incidence of osteoporosis increases 3-fold after 70 years of age in females and 2-fold after 80 years of age in males. The probability of osteoporosis in women is higher than the sum of the probabilities of other gynecological-specific diseases such as breast cancer, ovarian cancer and endometrial cancer. Therefore, the difference in sex gradually increases with age, and the difference in bone mass gradually increases. Gender considerations are required when considering the bone mass of a patient.
In order to make the bone of the patient healthier, the patient needs to do more exercises, and the exercises can increase local blood circulation and strengthen muscle strength. However, in order to avoid the problem of fracture during exercise, the exercise time needs to be controlled, so that the patient can exercise safely and strengthen physique. The patient can prevent or avoid fracture by strengthening daily bone health protection according to the estimated bone health condition, which is a problem to be solved by the person skilled in the art.
Disclosure of Invention
Therefore, the invention provides a bone health assessment method, a bone health assessment device and a storage medium, which are used for solving the problem that fracture is easy to occur in the prior art because the movement time of a patient cannot be accurately controlled.
In order to achieve the above object, the present invention provides the following technical solutions:
according to a first aspect of the present invention, there is provided a bone health assessment method comprising the steps of:
s1: acquiring vital sign data of a patient in a first motion state, a second motion state and a third motion state, wherein the first motion state is slow walking, the second motion state is fast walking, and the third motion state is jogging;
s2: predicting a rough probability interval of fracture of the patient in different motion states according to a first prediction model based on the vital sign data of the patient in different motion states;
s3: acquiring gender information and characteristic information about the gender of the patient, and determining a precise probability interval based on the predicted rough probability interval according to a second prediction model;
s4: and determining the accurate probability interval of the fracture of the patient in different motion states based on the predicted accurate probability interval of the fracture of the patient, and providing motion guidance comments.
Further, the establishing process of the first prediction model is as follows:
s201: acquiring first vital sign data of a patient in a first time period under different motion states;
s202: acquiring second vital sign data of the patient in a second time period under different movement states;
s203: acquiring third vital sign data of the patient in a third time period under different movement states;
s204: according to vital sign data of a patient in different time periods under different motion states, a first change prediction function of the vital sign data under a first motion state is established, a second change prediction function of the vital sign data under a second motion state is established, and a third change prediction function of the vital sign data under a third motion state is established;
s205: obtaining a first prediction model based on the first change prediction function, the second change prediction function and the third change prediction function, and predicting vital sign data of a patient changing along with time under different motion states;
s206: based on the predicted vital sign data under different motion states, obtaining a rough probability interval of fracture of the patient under different motion states according to a predicted fracture probability formula.
Further, the establishing process of the second prediction model is as follows:
s301: determining the sex of the patient, and acquiring first, second and third characteristic data related to the sex information of the patient based on the sex information of the patient;
s302: substituting the first, second and third characteristic data into the first, second and third change prediction functions based on the first, second and third characteristic data, and updating preset algorithms in different change prediction functions;
s303: based on the updated first change prediction function, second change prediction function and third change prediction function, a second prediction model is obtained, and vital sign data of the patient changing along with time under different motion states is obtained through correction prediction;
s304: and correcting the rough probability interval according to a predicted fracture probability formula based on vital sign data obtained by correction prediction under different motion states, so as to obtain an accurate probability interval of fracture of a patient under different motion states.
Further, the predicted fracture probability formula is:
wherein S is 1 Is a probability interval of fracture in a first motion state, x 1 For vital sign data predicted in a first motion state, t 1 For the movement time in the first movement state,a preset function in a first motion state; s is S 2 Is a probability interval of fracture in the second motion state, x 2 For vital sign data predicted in the second motion state, t 2 For the movement time in the second movement state +.>A preset function in a second motion state; s is S 3 Is a probability interval of fracture in the second motion state, x 3 For vital sign data predicted in the second motion state, t 3 For the movement time in the third movement state +.>Is a preset function in the third motion state.
Further, the step S4 specifically includes:
s401: acquiring a first accurate probability interval of fracture of a patient at different movement time in a first movement state, a second accurate probability interval of fracture at different movement time in a second movement state and a third accurate probability interval of fracture at different movement time in a third movement state;
s402: setting a first probability threshold interval in which fracture occurs in a first motion state, a second probability threshold interval in which fracture occurs in a second motion state, and a third probability threshold interval in which fracture occurs in a third motion state;
s403: if the first accurate probability interval falls into a first probability threshold interval, setting the motion time of the first accurate probability interval as a motion threshold in a first motion state; if the second accurate probability interval falls into a second probability threshold interval, setting the motion time of the second accurate probability interval as a motion threshold in a second motion state; if the third accurate probability interval falls into the third probability threshold interval, the motion time is set as the motion threshold in the third motion state.
Further, based on the motion threshold values in the different motion states obtained in the step S4, an optimal motion time in the different motion states is provided for the patient.
Further, the first motion state is slow walking, the second motion state is fast walking, and the third motion state is jogging.
According to a second aspect of the present invention, there is provided a bone health assessment device for implementing the steps of any one of the methods described above, comprising:
the information acquisition module is used for acquiring vital sign data of the patient in a first motion state, a second motion state and a third motion state;
the first information processing module is used for processing the vital sign data of different motion states to obtain rough probability intervals of fracture of the patient in different motion states;
the second information processing module is used for processing the sex information and the characteristic information about the sex of the patient to obtain accurate probability intervals of fracture of the patient in different motion states;
and the feedback module is used for providing exercise guidance comments based on the accurate probability interval of the fracture of the patient in different exercise states.
According to a third aspect of the present invention there is provided a storage medium for bone health assessment, the storage medium storing a computer program which when executed by a server performs the steps of any of the methods described above.
The invention has the following advantages:
the invention acquires vital sign data of a patient in a first movement state, a second movement state and a third movement state. Based on vital sign data of different motion states of the patient, a rough probability interval of fracture of the patient in the different motion states is predicted according to a first prediction model. And acquiring gender information and characteristic information about the gender of the patient, and determining an accurate probability interval based on the predicted rough probability interval according to the second prediction model. Based on the predicted accurate probability interval of the fracture of the patient, the accurate probability interval of the fracture of the patient in different motion states is determined, and the motion guidance opinion is provided.
And evaluating the bone health of the patient according to the first prediction model and the second prediction model, so as to predict the bone health of the patient. In order to enable the bone to be healthier for training, exercise guidance comments are provided, and exercise time is controlled, so that a patient can be trained safely, and physique is enhanced. The patient can prevent or avoid fracture by strengthening daily bone health protection according to the estimated bone health condition.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those of ordinary skill in the art that the drawings in the following description are exemplary only and that other implementations can be obtained from the extensions of the drawings provided without inventive effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the invention, which is defined by the claims, so that any structural modifications, changes in proportions, or adjustments of sizes, which do not affect the efficacy or the achievement of the present invention, should fall within the ambit of the technical disclosure.
FIG. 1 is a flow chart of a bone health assessment method provided by the invention;
fig. 2 is a specific flowchart of step S2 in the bone health assessment method provided by the present invention;
FIG. 3 is a flowchart showing a step S3 in the bone health assessment method according to the present invention;
fig. 4 is a specific flowchart of step S4 in the bone health assessment method provided by the present invention;
fig. 5 is a connection block diagram of the bone health assessment device provided by the invention.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
According to a first aspect of the present invention, as shown in fig. 1, there is provided a bone health assessment method comprising the steps of:
s1: acquiring vital sign data of a patient in a first motion state, a second motion state and a third motion state;
s2: predicting rough probability intervals of fracture of the patient in different motion states according to a first prediction model based on vital sign data of the patient in different motion states;
s3: acquiring gender information and characteristic information about the gender of the patient, and determining a precise probability interval based on the predicted rough probability interval according to a second prediction model;
s4: based on the predicted accurate probability interval of the fracture of the patient, the accurate probability interval of the fracture of the patient in different motion states is determined, and the motion guidance opinion is provided.
And evaluating the bone health of the patient according to the first prediction model and the second prediction model, so as to predict the bone health of the patient. In order to enable the bone to be healthier for training, exercise guidance comments are provided, and exercise time is controlled, so that a patient can be trained safely, and physique is enhanced. The patient can prevent or avoid fracture by strengthening daily bone health protection according to the estimated bone health condition.
In order to improve the bone health of the patient, the patient needs to perform proper exercise and train in a slow-walking, fast-walking or jogging mode. The first motion state is slow walking, the second motion state is fast walking, and the third motion state is jogging. The assessment system predicts vital sign data of the patient in jogging, fast walking and jogging motion states, respectively. Vital sign data include data indicating vital indexes such as age, body height, body weight, bone density, albumin content, calcium content, phosphorus content, alkaline phosphatase content, hemoglobin content, and lymphocyte content. And carrying out preliminary judgment on the bone health of the patient according to the predicted vital sign data. Since the sex of the patient has a significant influence on the bone health, the bone health of the patient is further judged by the acquired sex information of the patient. According to the accurate bone health condition, the exercise guidance is performed on the patient, so that the bone health of the patient is improved more pertinently, and the occurrence of fracture is avoided.
As shown in fig. 2, the first prediction model is established by:
s201: acquiring first vital sign data of a patient in a first time period under different motion states;
s202: acquiring second vital sign data of the patient in a second time period under different movement states;
s203: acquiring third vital sign data of the patient in a third time period under different movement states;
s204: according to vital sign data of a patient in different time periods under different motion states, a first change prediction function of the vital sign data under a first motion state is established, a second change prediction function of the vital sign data under a second motion state is established, and a third change prediction function of the vital sign data under a third motion state is established;
s205: based on the first change prediction function, the second change prediction function and the third change prediction function, a first prediction model is obtained, and vital sign data of the patient changing along with time under different motion states is predicted;
s206: based on the predicted vital sign data under different motion states, obtaining a rough probability interval of fracture of the patient under different motion states according to a predicted fracture probability formula.
And predicting vital sign data in the first motion state according to the first change prediction function, predicting vital sign data in the second motion state according to the second change prediction function, and predicting vital sign data in the third motion state according to the third change prediction function. And respectively obtaining a first rough probability interval in the first motion state, a second rough probability interval in the second motion state and a third rough probability interval in the third motion state according to the fracture probability prediction formula.
As shown in fig. 3, the second prediction model is established by:
s301: determining the sex of the patient, and acquiring first, second and third characteristic data related to the sex information of the patient based on the sex information of the patient;
s302: substituting the first feature data, the second feature data and the third feature data into the first change prediction function, the second change prediction function and the third change prediction function to update preset algorithms in different change prediction functions;
s303: based on the updated first change prediction function, second change prediction function and third change prediction function, a second prediction model is obtained, and vital sign data of the patient changing along with time under different motion states is obtained through correction prediction;
s304: and correcting the rough probability interval according to the predictive fracture probability formula based on the vital sign data under different motion states obtained by correction prediction, and obtaining the accurate probability interval of fracture of the patient under different motion states.
For a male sex of the patient, the first characteristic data is androgen content, the second characteristic data is femur length, and the third characteristic data is hip length. For a female sex of the patient, the first characteristic data is pregnancy, the second characteristic data is menopause, and the third characteristic data is estrogen content. The preset function in each variation prediction function is updated along with the variation of the gender information, so that the predicted fracture probability is calculated more finely.
Wherein, the predicted fracture probability formula is:
wherein S is 1 Is a probability interval of fracture in a first motion state, x 1 For vital sign data predicted in a first motion state, t 1 For the movement time in the first movement state,a preset function in a first motion state; s is S 2 Is a probability interval of fracture in the second motion state, x 2 For vital sign data predicted in the second motion state, t 2 For the movement time in the second movement state +.>A preset function in a second motion state; s is S 3 Is a probability interval of fracture in the second motion state, x 3 For vital sign data predicted in the second motion state, t 3 For the movement time in the third movement state +.>Is a preset function in the third motion state.
Obtaining a rough probability section S of fracture under a first motion state through a first prediction model 1 ' second movementRough probability interval S of fracture occurrence under state 2 ' coarse probability interval S of fracture in third motion state 3 '. The second prediction model corrects the rough probability interval to obtain an accurate probability interval S of fracture in the first motion state 1 ", an accurate probability interval S of fracture occurrence in the second motion state 2 ", an accurate probability interval S of fracture occurrence in a third motion state 3 ". The obtained rough probability interval is 30% -50%, the female precise probability interval is 40% -45%, and the male precise probability interval is 35% -40%.
As shown in fig. 4, step S4 specifically includes:
s401: acquiring a first accurate probability interval of fracture of a patient at different movement time in a first movement state, a second accurate probability interval of fracture at different movement time in a second movement state and a third accurate probability interval of fracture at different movement time in a third movement state;
s402: setting a first probability threshold interval in which fracture occurs in a first motion state, a second probability threshold interval in which fracture occurs in a second motion state, and a third probability threshold interval in which fracture occurs in a third motion state;
s403: if the first accurate probability interval falls into a first probability threshold interval, setting the motion time of the first accurate probability interval as a motion threshold in a first motion state; if the second accurate probability interval falls into a second probability threshold interval, setting the motion time of the second accurate probability interval as a motion threshold in a second motion state; if the third accurate probability interval falls into the third probability threshold interval, the motion time is set as the motion threshold in the third motion state.
Based on the motion threshold values in the different motion states obtained in step S4, the optimal motion time in the different motion states is provided for the patient. The motion time in the fracture probability formula is predicted to be updated in real time, and when the first accurate probability interval falls into a first probability threshold interval, the motion time is set to be the optimal motion time in a first motion state; if the second precise probability interval falls into the second probability threshold interval, setting the motion time to be the optimal motion time in the second motion state; if the third precise probability interval falls into the third probability threshold interval, setting the motion time to be the optimal motion time in the third motion state. So that the patient can safely train and strengthen physique. The patient can prevent or avoid fracture by strengthening daily bone health protection according to the estimated bone health condition.
According to a second aspect of the present invention, there is provided a bone health assessment device for implementing the steps of any of the methods described above, as shown in fig. 5, comprising:
the information acquisition module is used for acquiring vital sign data of the patient in a first motion state, a second motion state and a third motion state;
the first information processing module is used for processing vital sign data of different motion states to obtain rough probability intervals of fracture of a patient in different motion states;
the second information processing module is used for processing the sex information and the characteristic information about the sex of the patient to obtain accurate probability intervals of fracture of the patient in different motion states;
and the feedback module is used for providing exercise guidance comments based on the accurate probability intervals of the fracture of the patient in different exercise states.
The acquisition information module acquires vital sign data of the patient in a first motion state, a second motion state and a third motion state. The first information processing module predicts rough probability intervals of fracture of the patient in different motion states according to a first prediction model based on vital sign data of different motion states of the patient. The second information processing module acquires gender information of the patient and characteristic information about the gender, and determines an accurate probability interval based on the predicted rough probability interval according to the second prediction model. The feedback module determines the accurate probability interval of the fracture of the patient in different motion states based on the predicted accurate probability interval of the fracture of the patient and provides motion guidance comments.
According to a third aspect of the present invention there is provided a storage medium for bone health assessment, the storage medium storing a computer program which when executed by a server performs the steps of any of the methods described above.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.
Claims (10)
1. A bone health assessment method, comprising the steps of:
s1: acquiring vital sign data of a patient in a first motion state, a second motion state and a third motion state, wherein the first motion state is slow walking, the second motion state is fast walking, and the third motion state is jogging;
s2: predicting a rough probability interval of fracture of the patient in different motion states according to a first prediction model based on the vital sign data of the patient in different motion states;
s3: acquiring gender information and characteristic information about the gender of the patient, and determining a precise probability interval based on the predicted rough probability interval according to a second prediction model;
s4: and determining the accurate probability interval of the fracture of the patient in different motion states based on the predicted accurate probability interval of the fracture of the patient, and providing motion guidance comments.
2. The bone health assessment method according to claim 1, wherein the first prediction model is established by:
s201: acquiring first vital sign data of a patient in a first time period under different motion states;
s202: acquiring second vital sign data of the patient in a second time period under different movement states;
s203: acquiring third vital sign data of the patient in a third time period under different movement states;
s204: according to vital sign data of a patient in different time periods under different motion states, a first change prediction function of the vital sign data under a first motion state is established, a second change prediction function of the vital sign data under a second motion state is established, and a third change prediction function of the vital sign data under a third motion state is established;
s205: obtaining a first prediction model based on the first change prediction function, the second change prediction function and the third change prediction function, and predicting vital sign data of a patient changing along with time under different motion states;
s206: based on the predicted vital sign data under different motion states, obtaining a rough probability interval of fracture of the patient under different motion states according to a predicted fracture probability formula.
3. The bone health assessment method according to claim 2, wherein the second prediction model is established by:
s301: determining the sex of the patient, and acquiring first, second and third characteristic data related to the sex information of the patient based on the sex information of the patient;
s302: substituting the first, second and third characteristic data into the first, second and third change prediction functions based on the first, second and third characteristic data, and updating preset algorithms in different change prediction functions;
s303: based on the updated first change prediction function, second change prediction function and third change prediction function, a second prediction model is obtained, and vital sign data of the patient changing along with time under different motion states is obtained through correction prediction;
s304: and correcting the rough probability interval according to a predicted fracture probability formula based on vital sign data obtained by correction prediction under different motion states, so as to obtain an accurate probability interval of fracture of a patient under different motion states.
4. The bone health assessment method according to claim 3, wherein said predicted fracture probability formula is:
5. wherein S is 1 Is a probability interval of fracture in a first motion state, x 1 For vital sign data predicted in a first motion state, t 1 For the movement time in the first movement state,a preset function in a first motion state; s is S 2 Is a probability interval of fracture in the second motion state, x 2 For vital sign data predicted in the second motion state, t 2 For the movement time in the second movement state +.>A preset function in a second motion state; s is S 3 Is a probability interval of fracture in the second motion state, x 3 For vital sign data predicted in the second motion state, t 3 For the movement time in the third movement state +.>Is a preset function in the third motion state.
6. The bone health assessment method according to claim 4, wherein said step S4 specifically comprises:
s401: acquiring a first accurate probability interval of fracture of a patient at different movement time in a first movement state, a second accurate probability interval of fracture at different movement time in a second movement state and a third accurate probability interval of fracture at different movement time in a third movement state;
s402: setting a first probability threshold interval in which fracture occurs in a first motion state, a second probability threshold interval in which fracture occurs in a second motion state, and a third probability threshold interval in which fracture occurs in a third motion state;
s403: if the first accurate probability interval falls into a first probability threshold interval, setting the motion time of the first accurate probability interval as a motion threshold in a first motion state; if the second accurate probability interval falls into a second probability threshold interval, setting the motion time of the second accurate probability interval as a motion threshold in a second motion state; if the third accurate probability interval falls into the third probability threshold interval, the motion time is set as the motion threshold in the third motion state.
7. The bone health assessment method according to claim 5, wherein the patient is provided with an optimal movement time in different movement states based on the movement threshold values in different movement states obtained in said step S4.
8. The bone health assessment method of claim 1, wherein the first movement state is slow walking, the second movement state is fast walking, and the third movement state is jogging.
9. A bone health assessment device for implementing the steps of the method according to any one of claims 1-7, comprising:
the information acquisition module is used for acquiring vital sign data of the patient in a first motion state, a second motion state and a third motion state;
the first information processing module is used for processing the vital sign data of different motion states to obtain rough probability intervals of fracture of the patient in different motion states;
the second information processing module is used for processing the sex information and the characteristic information about the sex of the patient to obtain accurate probability intervals of fracture of the patient in different motion states;
and the feedback module is used for providing exercise guidance comments based on the accurate probability interval of the fracture of the patient in different exercise states.
10. A storage medium for bone health assessment, characterized in that the storage medium stores a computer program which, when executed by a server, implements the steps of the method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310841298.8A CN116564526B (en) | 2023-07-11 | 2023-07-11 | Bone health assessment method, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310841298.8A CN116564526B (en) | 2023-07-11 | 2023-07-11 | Bone health assessment method, device and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116564526A true CN116564526A (en) | 2023-08-08 |
| CN116564526B CN116564526B (en) | 2023-09-19 |
Family
ID=87486578
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310841298.8A Active CN116564526B (en) | 2023-07-11 | 2023-07-11 | Bone health assessment method, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116564526B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107536600A (en) * | 2017-08-24 | 2018-01-05 | 京东方科技集团股份有限公司 | Fracture index determines method and system |
| CN108498075A (en) * | 2017-02-24 | 2018-09-07 | 西门子保健有限责任公司 | The personalized assessment of bone health |
| CN108538393A (en) * | 2018-04-02 | 2018-09-14 | 重庆邮电大学 | Sclerotin quality evaluation expert system based on big data and prediction model method for building up |
| CN109256212A (en) * | 2018-08-17 | 2019-01-22 | 上海米因医疗器械科技有限公司 | Bone health assessment models construction method, device, equipment, medium and appraisal procedure |
| CN109597700A (en) * | 2018-12-03 | 2019-04-09 | 郑州云海信息技术有限公司 | A kind of disk life-span prediction method and relevant apparatus |
| CN110491520A (en) * | 2019-07-26 | 2019-11-22 | 北京邮电大学 | A kind of construction method of the sclerotin status assessment model based on semi-supervised learning |
| CN111951966A (en) * | 2020-08-05 | 2020-11-17 | 宁波市奉化区人民医院医共体 | Method for predicting postoperative complications of osteoporotic fracture based on possum scoring system |
| CN112164464A (en) * | 2020-11-03 | 2021-01-01 | 上海市同济医院 | Establishment method and application of osteoporosis risk prediction model for type 2 diabetes patients |
-
2023
- 2023-07-11 CN CN202310841298.8A patent/CN116564526B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108498075A (en) * | 2017-02-24 | 2018-09-07 | 西门子保健有限责任公司 | The personalized assessment of bone health |
| CN107536600A (en) * | 2017-08-24 | 2018-01-05 | 京东方科技集团股份有限公司 | Fracture index determines method and system |
| CN108538393A (en) * | 2018-04-02 | 2018-09-14 | 重庆邮电大学 | Sclerotin quality evaluation expert system based on big data and prediction model method for building up |
| CN109256212A (en) * | 2018-08-17 | 2019-01-22 | 上海米因医疗器械科技有限公司 | Bone health assessment models construction method, device, equipment, medium and appraisal procedure |
| CN109597700A (en) * | 2018-12-03 | 2019-04-09 | 郑州云海信息技术有限公司 | A kind of disk life-span prediction method and relevant apparatus |
| CN110491520A (en) * | 2019-07-26 | 2019-11-22 | 北京邮电大学 | A kind of construction method of the sclerotin status assessment model based on semi-supervised learning |
| CN111951966A (en) * | 2020-08-05 | 2020-11-17 | 宁波市奉化区人民医院医共体 | Method for predicting postoperative complications of osteoporotic fracture based on possum scoring system |
| CN112164464A (en) * | 2020-11-03 | 2021-01-01 | 上海市同济医院 | Establishment method and application of osteoporosis risk prediction model for type 2 diabetes patients |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116564526B (en) | 2023-09-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| DeSilva et al. | Estimation of sex from the metric assessment of digital hand radiographs in a Western Australian population | |
| CN112274164B (en) | Scoliosis prediction method, scoliosis prediction device, electronic device, and storage medium | |
| CN109545322B (en) | Information pushing method, device, server and readable medium based on health data | |
| CN109273074B (en) | Network model adjusting method and equipment for medical image | |
| CN107767874A (en) | A kind of baby crying sound identification reminding method and system | |
| US20230108389A1 (en) | Data processing method and apparatus, device and medium | |
| CN110619957A (en) | Cerebral stroke screening method and device, storage medium and equipment | |
| CN116564526B (en) | Bone health assessment method, device and storage medium | |
| CN113689359A (en) | Image artifact removing model and training method and system thereof | |
| CN115279270A (en) | Prediction device, prediction system, control method, and control program | |
| CN114121270A (en) | Disease risk grade prediction method and device | |
| CN112604186A (en) | Respiratory motion prediction method | |
| CN114881943A (en) | Artificial intelligence-based brain age prediction method, device, equipment and storage medium | |
| CN116825335B (en) | Method and apparatus for predictive model training of prognostic outcome of parkinson's patient gait | |
| CN116485793B (en) | Teenager scoliosis orthopedic operation effect prediction method | |
| CN111000564B (en) | Wearable device and method for tumble monitoring | |
| CN117237304A (en) | Bone age assessment method and system for children with autonomous learning ability | |
| US20230126896A1 (en) | Method for providing basic data for diagnosis, and system therefor | |
| CN114881941A (en) | Spine health change prediction method and system based on big data | |
| CN118072958B (en) | Diabetes risk prediction optimization system based on knowledge graph | |
| Shim et al. | Statistical classification methods for estimating sex based on five skull traits: A nonmetric assessment using 3D CT models. | |
| CN113409920B (en) | Data transmission management method and system | |
| EP4318494A1 (en) | Method and apparatus for providing confidence information on result of artificial intelligence model | |
| EP4331483A1 (en) | Emotion estimating device, emotion estimating system, and emotion estimating method | |
| CN118844986A (en) | Gait feature range determination method, device, apparatus, medium and program product |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |